An Expectation Conditional Maximization Approach for Gaussian Graphical Models
From MaRDI portal
Publication:3391200
DOI10.1080/10618600.2019.1609976OpenAlexW2963871764WikidataQ100504124 ScholiaQ100504124MaRDI QIDQ3391200
Tyler H. McCormick, Zehang Richard Li
Publication date: 28 March 2022
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7540244
Related Items (8)
Bayesian inference of clustering and multiple Gaussian graphical models selection ⋮ Rejoinder to the discussion of ``Bayesian graphical models for modern biological applications ⋮ Marginal Distance and Hilbert-Schmidt Covariances-Based Independence Tests for Multivariate Functional Data ⋮ Latent Network Estimation and Variable Selection for Compositional Data Via Variational EM ⋮ Bayesian analysis of nonparanormal graphical models using rank-likelihood ⋮ Incorporating grouping information into Bayesian Gaussian graphical model selection ⋮ Bayesian sparse seemingly unrelated regressions model with variable selection and covariance estimation via the horseshoe+ ⋮ Distributed testing on mutual independence of massive multivariate data
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- A sparse conditional Gaussian graphical model for analysis of genetical genomics data
- Extending the rank likelihood for semiparametric copula estimation
- Sparse inverse covariance estimation with the graphical lasso
- Scaling it up: stochastic search structure learning in graphical models
- Regularized rank-based estimation of high-dimensional nonparanormal graphical models
- Inferring metabolic networks using the Bayesian adaptive graphical Lasso with informative priors
- Optimal rates of convergence for covariance matrix estimation
- Estimating the dimension of a model
- An introduction to copulas. Properties and applications
- The stochastic EM algorithm: Estimation and asymptotic results
- Convergence of a stochastic approximation version of the EM algorithm
- Efficient Gaussian graphical model determination under \(G\)-Wishart prior distributions
- The graphical lasso: new insights and alternatives
- Sparse permutation invariant covariance estimation
- Network exploration via the adaptive LASSO and SCAD penalties
- Experiments in stochastic computation for high-dimensional graphical models
- High-dimensional graphs and variable selection with the Lasso
- Bayesian graphical Lasso models and efficient posterior computation
- ggplot2
- Particle EM for Variable Selection
- Maximum likelihood estimation via the ECM algorithm: A general framework
- Bayesian Inference for General Gaussian Graphical Models With Application to Multivariate Lattice Data
- Simultaneous Variable and Covariance Selection With the Multivariate Spike-and-Slab LASSO
- Model selection and estimation in the Gaussian graphical model
- Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models
- Detecting Differentially Expressed Genes in Microarrays Using Bayesian Model Selection
- EMVS: The EM Approach to Bayesian Variable Selection
- Bayesian Joint Modeling of Multiple Brain Functional Networks
- The huge Package for High-dimensional Undirected Graph Estimation in R
This page was built for publication: An Expectation Conditional Maximization Approach for Gaussian Graphical Models
